WO2016075331A3 - Method and system for purely geometric machine learning based fractional flow reserve - Google Patents

Method and system for purely geometric machine learning based fractional flow reserve Download PDF

Info

Publication number
WO2016075331A3
WO2016075331A3 PCT/EP2015/076685 EP2015076685W WO2016075331A3 WO 2016075331 A3 WO2016075331 A3 WO 2016075331A3 EP 2015076685 W EP2015076685 W EP 2015076685W WO 2016075331 A3 WO2016075331 A3 WO 2016075331A3
Authority
WO
WIPO (PCT)
Prior art keywords
patient
extracted
learning based
arterial tree
flow reserve
Prior art date
Application number
PCT/EP2015/076685
Other languages
French (fr)
Other versions
WO2016075331A2 (en
Inventor
Lucian Mihai ITU
Tiziano Passerini
Saikiran Rapaka
Chris Schwemmer
Max Schöbinger
Puneet Sharma
Original Assignee
Siemens Healthcare Gmbh
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US14/804,609 external-priority patent/US9349178B1/en
Application filed by Siemens Healthcare Gmbh filed Critical Siemens Healthcare Gmbh
Priority to EP24187076.5A priority Critical patent/EP4418206A2/en
Priority to US15/508,220 priority patent/US10463336B2/en
Priority to CN201580061934.9A priority patent/CN107427268B/en
Priority to EP15804080.8A priority patent/EP3218872A2/en
Priority to JP2017525590A priority patent/JP6539736B2/en
Publication of WO2016075331A2 publication Critical patent/WO2016075331A2/en
Publication of WO2016075331A3 publication Critical patent/WO2016075331A3/en

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/50—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications
    • A61B6/504—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment specially adapted for specific body parts; specially adapted for specific clinical applications for diagnosis of blood vessels, e.g. by angiography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/52—Devices using data or image processing specially adapted for radiation diagnosis
    • A61B6/5211—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data
    • A61B6/5217—Devices using data or image processing specially adapted for radiation diagnosis involving processing of medical diagnostic data extracting a diagnostic or physiological parameter from medical diagnostic data
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B6/00—Apparatus or devices for radiation diagnosis; Apparatus or devices for radiation diagnosis combined with radiation therapy equipment
    • A61B6/02—Arrangements for diagnosis sequentially in different planes; Stereoscopic radiation diagnosis
    • A61B6/03—Computed tomography [CT]
    • A61B6/032—Transmission computed tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • G06T2207/30104—Vascular flow; Blood flow; Perfusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/14—Vascular patterns

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Medical Informatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Pathology (AREA)
  • Radiology & Medical Imaging (AREA)
  • Physics & Mathematics (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Biomedical Technology (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Biophysics (AREA)
  • High Energy & Nuclear Physics (AREA)
  • Optics & Photonics (AREA)
  • Veterinary Medicine (AREA)
  • Animal Behavior & Ethology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Physiology (AREA)
  • Vascular Medicine (AREA)
  • Dentistry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Apparatus For Radiation Diagnosis (AREA)
  • Image Analysis (AREA)

Abstract

A method and system for determining hemodynamic indices, such as fractional flow reserve (FFR), for a location of interest in a coronary artery of a patient is disclosed. Medical image data of a patient is received. Patient-specific coronary arterial tree geometry of the patient is extracted from the medical image data. Geometric features are extracted from the patient-specific coronary arterial tree geometry of the patient. A hemodynamic index, such as FFR, is computed for a location of interest in the patient-specific coronary arterial tree based on the extracted geometric features using a trained machine-learning based surrogate model. The machine-learning based surrogate model is trained based on geometric features extracted from synthetically generated coronary arterial tree geometries.
PCT/EP2015/076685 2014-11-14 2015-11-16 Method and system for purely geometric machine learning based fractional flow reserve WO2016075331A2 (en)

Priority Applications (5)

Application Number Priority Date Filing Date Title
EP24187076.5A EP4418206A2 (en) 2014-11-14 2015-11-16 Method and system for purely geometric machine learning based fractional flow reserve
US15/508,220 US10463336B2 (en) 2014-11-14 2015-11-16 Method and system for purely geometric machine learning based fractional flow reserve
CN201580061934.9A CN107427268B (en) 2014-11-14 2015-11-16 Method and system for fractional flow reserve based on pure geometry machine learning
EP15804080.8A EP3218872A2 (en) 2014-11-14 2015-11-16 Method and system for purely geometric machine learning based fractional flow reserve
JP2017525590A JP6539736B2 (en) 2014-11-14 2015-11-16 Method and system for determining blood flow reserve ratio based on pure geometric machine learning

Applications Claiming Priority (8)

Application Number Priority Date Filing Date Title
US201462079641P 2014-11-14 2014-11-14
US62/079,641 2014-11-14
US201462083373P 2014-11-24 2014-11-24
US62/083,373 2014-11-24
US14/804,609 2015-07-21
US14/804,609 US9349178B1 (en) 2014-11-24 2015-07-21 Synthetic data-driven hemodynamic determination in medical imaging
US14/876,852 US9918690B2 (en) 2014-11-24 2015-10-07 Synthetic data-driven hemodynamic determination in medical imaging
US14/876,852 2015-10-07

Publications (2)

Publication Number Publication Date
WO2016075331A2 WO2016075331A2 (en) 2016-05-19
WO2016075331A3 true WO2016075331A3 (en) 2016-08-11

Family

ID=54771075

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2015/076685 WO2016075331A2 (en) 2014-11-14 2015-11-16 Method and system for purely geometric machine learning based fractional flow reserve

Country Status (1)

Country Link
WO (1) WO2016075331A2 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108784655B (en) * 2017-04-28 2021-11-09 西门子保健有限责任公司 Rapid assessment and outcome analysis for medical patients

Families Citing this family (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
ES2836104T3 (en) * 2016-12-15 2021-06-24 Sintef Tto As Method and process to provide a specific computational model of a subject used to support the decision and make the diagnosis of cardiovascular diseases
FR3062498B1 (en) * 2017-02-02 2019-06-07 Casis - Cardiac Simulation & Imaging Software SYSTEM AND METHOD FOR EVALUATION OF VASCULAR RISKS
US10475214B2 (en) 2017-04-05 2019-11-12 General Electric Company Tomographic reconstruction based on deep learning
US10825167B2 (en) 2017-04-28 2020-11-03 Siemens Healthcare Gmbh Rapid assessment and outcome analysis for medical patients
EP3404667B1 (en) * 2017-05-19 2024-02-28 Siemens Healthineers AG Learning based methods for personalized assessment, long-term prediction and management of atherosclerosis
US10483006B2 (en) 2017-05-19 2019-11-19 Siemens Healthcare Gmbh Learning based methods for personalized assessment, long-term prediction and management of atherosclerosis
WO2019002510A1 (en) 2017-06-30 2019-01-03 Koninklijke Philips N.V. Machine learning spectral ffr-ct
US10909676B2 (en) 2017-07-12 2021-02-02 Siemens Healthcare Gmbh Method and system for clinical decision support with local and remote analytics
CN109256205B (en) 2017-07-12 2022-07-05 西门子保健有限责任公司 Method and system for clinical decision support with local and remote analytics
WO2019025270A1 (en) * 2017-08-01 2019-02-07 Siemens Healthcare Gmbh Non-invasive assessment and therapy guidance for coronary artery disease in diffuse and tandem lesions
US11589924B2 (en) 2017-08-01 2023-02-28 Siemens Healthcare Gmbh Non-invasive assessment and therapy guidance for coronary artery disease in diffuse and tandem lesions
EP3489893B1 (en) 2017-11-22 2020-06-24 Siemens Healthcare GmbH Method and system for assessing a haemodynamic parameter
WO2019170561A1 (en) 2018-03-08 2019-09-12 Koninklijke Philips N.V. Resolving and steering decision foci in machine learning-based vascular imaging
US11389130B2 (en) 2018-05-02 2022-07-19 Siemens Healthcare Gmbh System and methods for fast computation of computed tomography based fractional flow reserve
EP3564963B1 (en) * 2018-05-02 2024-10-23 Siemens Healthineers AG System and methods for fast computation of computed tomography based fractional flow reserve
CN109036551B (en) * 2018-07-10 2021-05-11 北京心世纪医疗科技有限公司 Coronary artery physiological index relation establishing and applying method and device
EP3751580B1 (en) * 2019-06-11 2024-04-03 Siemens Healthineers AG Hemodynamic analysis of vessels using recurrent neural network
EP3819909A1 (en) * 2019-11-05 2021-05-12 Siemens Healthcare GmbH Assessment of collateral coronary arteries
US11145057B2 (en) 2019-11-05 2021-10-12 Siemens Healthcare Gmbh Assessment of collateral coronary arteries
DE102020200750A1 (en) * 2020-01-22 2021-07-22 Siemens Healthcare Gmbh Providing a blood flow parameter set of a vascular malformation
CN111445449B (en) * 2020-03-19 2024-03-01 上海联影智能医疗科技有限公司 Method, device, computer equipment and storage medium for classifying region of interest
DE102020210192A1 (en) * 2020-08-12 2022-02-17 Siemens Healthcare Gmbh Providing a synthetically contrasted scene
EP4428871A1 (en) * 2023-03-09 2024-09-11 Hemolens Diagnostics Spólka Z Ograniczona Odpowiedzialnoscia A method of training an artificial deep neural network for estimation of hemodynamic parameter, a method of estimation of hemodynamic parameter, computer program products and computer systems
CN117036531B (en) * 2023-10-10 2023-12-22 杭州脉流科技有限公司 Method, system and storage medium for obtaining fractional flow reserve based on GPU

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140073976A1 (en) * 2012-09-12 2014-03-13 Heartflow, Inc. Systems and methods for estimating ischemia and blood flow characteristics from vessel geometry and physiology
US20150112182A1 (en) * 2013-10-17 2015-04-23 Siemens Aktiengesellschaft Method and System for Machine Learning Based Assessment of Fractional Flow Reserve
EP2949268A1 (en) * 2014-05-29 2015-12-02 Siemens Aktiengesellschaft System and method for mapping patient data from one physiological state to another physiological state

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140073976A1 (en) * 2012-09-12 2014-03-13 Heartflow, Inc. Systems and methods for estimating ischemia and blood flow characteristics from vessel geometry and physiology
US20150112182A1 (en) * 2013-10-17 2015-04-23 Siemens Aktiengesellschaft Method and System for Machine Learning Based Assessment of Fractional Flow Reserve
EP2949268A1 (en) * 2014-05-29 2015-12-02 Siemens Aktiengesellschaft System and method for mapping patient data from one physiological state to another physiological state

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108784655B (en) * 2017-04-28 2021-11-09 西门子保健有限责任公司 Rapid assessment and outcome analysis for medical patients

Also Published As

Publication number Publication date
WO2016075331A2 (en) 2016-05-19

Similar Documents

Publication Publication Date Title
WO2016075331A3 (en) Method and system for purely geometric machine learning based fractional flow reserve
EP2966615A3 (en) Method and system for improved hemodynamic computation in coronary arteries
NZ730197A (en) Bayesian causal relationship network models for healthcare diagnosis and treatment based on patient data
IL248870B (en) Systems and methods for reporting blood flow characteristics
WO2015030998A3 (en) Systems and methods for predicting location, onset, and/or change of coronary lesions
WO2014210603A3 (en) Systems and method for evaluating and selecting a healthcare professional
EP2930637A3 (en) Patient-need prediction system
JP2017510350A5 (en)
EP3704491A4 (en) Diagnostic and prognostic methods for peripheral arterial diseases, aortic stenosis, and outcomes
MX2016009335A (en) Portable electronic hemodynamic sensor systems.
EP4343707A3 (en) Indication-dependent display of a medical image
RU2016142360A (en) DEVICE FOR PROCESSING AND METHOD FOR PROCESSING DATA ON THE HEART ACTIVITY OF A LIVING BEING
MX2017012039A (en) System and method for medical procedure planning.
WO2015022649A3 (en) Modeling of patient risk factors at discharge
MX2017008904A (en) Blood based biomarkers for diagnosing atherosclerotic coronary artery disease.
EP3569150A3 (en) Systems and methods for predicting coronary plaque vulnerability from patient-specific anatomic image data
WO2012109618A3 (en) System and method for planning a patient-specific cardiac procedure
EP2690598A3 (en) Method and apparatus for determining blood flow required, method and apparatus for producing blood flow image, and method and apparatus for processing myocardial perfusion image
WO2015153254A8 (en) Extracorporeal blood treatment alarm docking
WO2013144803A3 (en) System and method for improving neurologist's workflow on alzheimer's disease
WO2014182930A3 (en) Systems and methods for administering health care systems
RU2015119240A (en) SYSTEM AND METHOD OF HEALTH CARE
SA521422498B1 (en) A device for modeling of hemodynamic parameters in coronary arteries
SG11201702649PA (en) Method and system for predicting continous cardiac output (cco) of a patient based on physiological data
WO2014140848A3 (en) Systems and methods for providing retail process analytics information based on physiological indicator data

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15804080

Country of ref document: EP

Kind code of ref document: A2

WWE Wipo information: entry into national phase

Ref document number: 15508220

Country of ref document: US

REEP Request for entry into the european phase

Ref document number: 2015804080

Country of ref document: EP

ENP Entry into the national phase

Ref document number: 2017525590

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 201580061934.9

Country of ref document: CN